The objective of this research is introduce a new machine learning ensemble approach that is a hybridization of Bagging ensemble (BE) and Logistic Model Trees (LMTree), named as BE-LMtree, for improving the performance of the landslide susceptibility model. The LMTree is a relatively new machine learning algorithm that was rarely explored for landslide study, whereas BE is an ensemble framework that has proven highly efficient for landslide modeling. Upper Reaches Area of Red River Basin (URRB) in Northwest region of Viet Nam was employed as a case study. For this work, a GIS database for the URRB area has been established, which contains a total of 255 landslide polygons and eight predisposing factors i.e., slope, aspect, elevation, land cover, soil type, lithology, distance to fault, and distance to river. The database was then used to construct and validate the proposed BE-LMTree model. Quality of the final BE-LMTree model was checked using confusion matrix and a set of statistical measures. The result showed that the performance of the proposed BE-LMTree model is high with the classification accuracy is 93.81% on the training dataset and the prediction capability is 83.4% on the on the validation dataset. When compared to the support vector machine model and the LMTree model, the proposed BE-LMTree model performs better; therefore, we concluded that the BE-LMTree could prove to be a new efficient tool that should be used for landslide modeling. This research could provide useful results for landslide modeling in landslide prone areas.

This paper presents the results of the design and implementation of a new geological tool, named as HUMGEOSTAT, for advanced geostatistical analysis of mineral deposit, a case study at the Sin Quyen, one of the important mines for the Vietnam mining industry in Vietnam. The tool is developed based on the Stanford Geostatistical Modeling Software (SgeMS) platform, which is an open-source computer package for solving problems involving spatially related variables. The tool is capable of supporting users in reducing uncertainty and improving the quality of mining exploration tasks. The tool has all the functions needed to solve a complete geostatistical issues i.e. Variogram models (such as Spherical, Exponential, Gausian, Linear and Pure nugget effect); and four commonly used Kriging methods (Ordinary Kriging, Simple Kriging, Indicator Kriging, Co-Kriging). The tool was applied to test various types of input data and particularly for complex copper ore bodies of Sin Quyen mine. The results of analysis of orebody spatial structure has found that Cu content has non-uniform change, regional anisotropy in north-south (0±22.5⁰) direction, and anisotropic index is around 1.3. The mineral reserves are estimated according to sub-blocks and points in the orebody-space. The results indicate that the Ordinary Kriging (OK) has reliable results, variance estimation is small; the predicted result gained from OK have strong correlation (K=0.905) with ore samples. HUMGEOSTAT was applied at the Sin Quyen mine and various coal mines, the results were adapted with reality of mines.